Machine Learning for Data Mining in Plant Research

The integration of machine learning (ML) with data mining techniques is revolutionizing plant research, facilitating the uncovering of key insights from increasingly intricate and voluminous datasets. Here the foundational concepts of ML and their practical applications in analyzing diverse plant data types, including genomic, phenotypic, environmental, and sensor-derived information, are explored. Commonly used ML algorithms in the plant sciences and their value for objectives such as trait prediction, disease detection, yield estimation, and stress response analysis are highlighted. Drawing upon real-world case studies, the chapter illustrates how ML is being employed to enhance breeding efficiency, precision agriculture, and climate-resilient crop development. Fundamental tasks such as data heterogeneity, model interpretability, computational limitations, and ethical concerns are critically examined. Furthermore, the chapter outlines future directions and emerging trends, including explainable AI, multi-omics data integration, federated learning, and real-time decision support systems. Collectively, the chapter offers a broad overview of how ML is shaping the future of plant science and advancing sustainable agricultural innovation.